This translates HawkEars confidence scores into probabilities using species-specific logistic regression models.

DA II Case Study — Exploratory & Inferential Analytics using real sales data from Wins_O_Win Nigeria ltd Submitted to Lagos Business School, Prof Bongo Adi, April 2026.

TX_IP_21to24 %>% filter(`Admission Type` == "Elective" & has_ADRD == 1) %>% dplyr::select(RECORD_ID, has_ADRD, `Admission Type`, PRINC_DIAG_CODE) %>% melt(., id.vars = c("RECORD_ID", "Admission Type", "has_ADRD"), variable.name = "COLNAME", value.name = "DIAG_CODE") %>% left_join(., ICD_DX_TO_CCSR, by=c("DIAG_CODE"="ICD10")) %>% dplyr::select(`Admission Type`, CCSR_desc, has_ADRD) %>% group_by(`Admission Type`, has_ADRD) %>% mutate(PRINC_DIAG_CODE = fct_infreq(CCSR_desc) %>% fct_lump_n(n = 3, other_level = "Other") ) %>% dplyr::select(PRINC_DIAG_CODE, has_ADRD, `Admission Type`) %>% dplyr::rename(., "Principal Diagnosis"=1) %>% tbl_summary( missing_text = "NA", by="Admission Type", include = "Principal Diagnosis") ->elective_top3princ TX_IP_21to24 %>% filter(`Admission Type` == "Emergency" & has_ADRD == 1) %>% dplyr::select(RECORD_ID, has_ADRD, `Admission Type`, PRINC_DIAG_CODE) %>% melt(., id.vars = c("RECORD_ID", "Admission Type", "has_ADRD"), variable.name = "COLNAME", value.name = "DIAG_CODE") %>% left_join(., ICD_DX_TO_CCSR, by=c("DIAG_CODE"="ICD10")) %>% dplyr::select(`Admission Type`, CCSR_desc, has_ADRD) %>% group_by(`Admission Type`, has_ADRD) %>% mutate(PRINC_DIAG_CODE = fct_infreq(CCSR_desc) %>% fct_lump_n(n = 3, other_level = "Other") ) %>% dplyr::select(PRINC_DIAG_CODE, has_ADRD, `Admission Type`) %>% dplyr::rename(., "Principal Diagnosis"=1) %>% tbl_summary( missing_text = "NA", by="Admission Type", include = "Principal Diagnosis") ->emergency_top3princ TX_IP_21to24 %>% filter(`Admission Type` == "Trauma" & has_ADRD == 1) %>% dplyr::select(RECORD_ID, has_ADRD, `Admission Type`, PRINC_DIAG_CODE) %>% melt(., id.vars = c("RECORD_ID", "Admission Type", "has_ADRD"), variable.name = "COLNAME", value.name = "DIAG_CODE") %>% left_join(., ICD_DX_TO_CCSR, by=c("DIAG_CODE"="ICD10")) %>% dplyr::select(`Admission Type`, CCSR_desc, has_ADRD) %>% group_by(`Admission Type`, has_ADRD) %>% mutate(PRINC_DIAG_CODE = fct_infreq(CCSR_desc) %>% fct_lump_n(n = 3, other_level = "Other") ) %>% dplyr::select(PRINC_DIAG_CODE, has_ADRD, `Admission Type`) %>% dplyr::rename(., "Principal Diagnosis"=1) %>% tbl_summary( missing_text = "NA", by="Admission Type", include = "Principal Diagnosis") ->trauma_top3princ TX_IP_21to24 %>% filter(`Admission Type` == "Urgent" & has_ADRD == 1) %>% dplyr::select(RECORD_ID, has_ADRD, `Admission Type`, PRINC_DIAG_CODE) %>% melt(., id.vars = c("RECORD_ID", "Admission Type", "has_ADRD"), variable.name = "COLNAME", value.name = "DIAG_CODE") %>% left_join(., ICD_DX_TO_CCSR, by=c("DIAG_CODE"="ICD10")) %>% dplyr::select(`Admission Type`, CCSR_desc, has_ADRD) %>% group_by(`Admission Type`, has_ADRD) %>% mutate(PRINC_DIAG_CODE = fct_infreq(CCSR_desc) %>% fct_lump_n(n = 3, other_level = "Other") ) %>% dplyr::select(PRINC_DIAG_CODE, has_ADRD, `Admission Type`) %>% dplyr::rename(., "Principal Diagnosis"=1) %>% tbl_summary( missing_text = "NA", by="Admission Type", include = "Principal Diagnosis") ->urgent_top3princ TX_IP_21to24 %>% filter(`Admission Type` == "Not Available" & has_ADRD == 1) %>% dplyr::select(RECORD_ID, has_ADRD, `Admission Type`, PRINC_DIAG_CODE) %>% melt(., id.vars = c("RECORD_ID", "Admission Type", "has_ADRD"), variable.name = "COLNAME", value.name = "DIAG_CODE") %>% left_join(., ICD_DX_TO_CCSR, by=c("DIAG_CODE"="ICD10")) %>% dplyr::select(`Admission Type`, CCSR_desc, has_ADRD) %>% group_by(`Admission Type`, has_ADRD) %>% mutate(PRINC_DIAG_CODE = fct_infreq(CCSR_desc) %>% fct_lump_n(n = 3, other_level = "Other") ) %>% dplyr::select(PRINC_DIAG_CODE, has_ADRD, `Admission Type`) %>% dplyr::rename(., "Principal Diagnosis"=1) %>% tbl_summary( missing_text = "NA", by="Admission Type", include = "Principal Diagnosis") ->NA_top3princ tbl_stack(list(emergency_top3princ, elective_top3princ, urgent_top3princ, trauma_top3princ, NA_top3princ), group_header = c("Emergency", "Elective", "Urgent", "Trauma", "Not Available")) %>% modify_header(update = list( stat_1 ~ "**Frequency**")) %>% modify_caption("**Table 3. Top 3 CCSR Groupings for Principal Diagnoses of Inpatients 65+ in the RGV**") %>% as_gt() %>% gt::tab_style( style = gt::cell_text(weight = "bold"), locations = gt::cells_row_groups(groups = everything()) )

Operational Analytics of Network Availability and MTTR at IHS Towers Nigeria

La industria musical ha experimentado una de las transformaciones más profundas de su historia en las últimas dos décadas. El paso del formato físico al digital, y posteriormente al streaming, no solo cambió cómo escuchamos música, sino también cómo se genera valor económico en torno a ella.